wine


These data are the results of a chemical analysis of wines grown in the same
region in Italy but derived from three different cultivars. The analysis determined
the quantities of 13 constituents found in each of the three types of wines.


datasets

Provides a collection of self-labeled techniques for semi-supervised classification. In semi-supervised classification, both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. This package implements a collection of self-labeled techniques to construct a distance-based classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a suitable base classifier and distance measure. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.

Christoph Bergmeir

Semi-Supervised Classification Methods

wine function

A data frame with 178 rows and 14 variables including the class.

Format

<a href="https://archive.ics.uci.edu/ml/datasets/Wine">https://archive.ics.uci.edu/ml/datasets/Wine</a>

Source

<a href = 'https://archive.ics.uci.edu/ml/datasets/Wine'>https://archive.ics.uci.edu/ml/datasets/Wine</a>

wine: Wine recognition data

Description

Usage

Arguments

Format

Source

Details